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•0-PLOS I o-^E 

Stepwise Threshold Clustering: A New Method for ^ 
Genotyping MHC Loci Using Next-Generation cros^k 
Sequencing Technology 

William E. Stutz^% Daniel I. Bolnick^ 

1 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, United States of America, 2 Howard Hughes IVledical Institute & Section of 
Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America 


Genes of the vertebrate major histocompatibility complex (MHC) are of great Interest to biologists because of their 
important role in immunity and disease, and their extremely high levels of genetic diversity. Next generation sequencing 
(NGS) technologies are quickly becoming the method of choice for high-throughput genotyping of multi-locus templates 
like MHC in non-model organisms. Previous approaches to genotyping MHC genes using NGS technologies suffer from two 
problems:!) a "gray zone" where low frequency alleles and high frequency artifacts can be difficult to disentangle and 2) a 
similar sequence problem, where very similar alleles can be difficult to distinguish as two distinct alleles. Here were present 
a new method for genotyping MHC loci - Stepwise Threshold Clustering (STC) - that addresses these problems by taking 
full advantage of the increase in sequence data provided by NGS technologies. Unlike previous approaches for genotyping 
MHC with NGS data that attempt to classify individual sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering 
algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based 
criteria to clusters rather than individual sequences, STC is able to successfully identify clusters of sequences that 
correspond to individual or similar alleles present in the genomes of individual samples. Furthermore, STC does not require 
duplicate runs of all samples, increasing the number of samples that can be genotyped in a given project. We show how the 
STC method works using a single sample library. We then apply STC to 295 threespine stickleback {Casterosteus aculeatus) 
samples from four populations and show that neighboring populations differ significantly in MHC allele pools. We show that 
STC is a reliable, accurate, efficient, and flexible method for genotyping MHC that will be of use to biologists interested in a 
variety of downstream applications. 

Citation: Stutz WE, Bolnick Dl (2014) Stepwise Threshold Clustering: A New iVlethod for Genotyping MHC Loci Using Next-Generation Sequencing 
Technology. PLoS ONE 9(7): el00587. doi:10.1371/journal.pone.0100587 

Editor: Sean Rogers, University of Calgary, Canada 

Received February 26, 2014; Accepted IVlay 26, 2014; Published July 18, 2014 

Copyright: © 2014 Stutz, Bolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits 
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 

Funding: Funding comes from the Howard Hughes IVledical Institute ( Dan Bolnick (co-author) is an HHMI young investigator. The funders had 
no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 

Competing interests: The authors have declared that no competing interests exist. 

* Email: 


The major histocompatibility complex (MHC) is a genomic 
region (or set of regions) unique to vertebrates that contains genes 
crucial for the proper functioning of the adaptive immune system. 
Of particular interest are the MHC class I and class II loci, which 
encode cell surface receptors that bind and present antigens (both 
self and non-self derived) to immune-effector cells [1,2]. The 
resulting interaction between MHC receptors, antigens, and T- 
ceUs leads to both self-tolerance via negative selection on auto- 
reactive T cell variants and to activation of cell-mediated immune 
responses when antigens are non-self derived peptides. Conse- 
quently, MHC loci are of great interest in both the study of 
pathogen resistance and in the study of autoimmune disorders. 
Both MHC class I and class II loci are noteworthy because of their 
diversity within and among individuals [3-6] , and MHC loci have 
served as a model genetic system for exploring questions about the 
selective mechanisms maintaining genetic diversity in natural 
populations [7-14]. Understanding the causes and consequences 
of MHC variation also has strong implications for biologists 
interested in the evolution and epidemiology of infectious disease 

[15-17] and the conservation of endangered populations [18,19]. 
In addition to its immunological importance, variation at MHC 
loci has also been shown to influence mate-choice decisions in 
many animals, allowing the discrimination of related and 
vmrelated individuals and of immunologically compatible and 
incompatible mates [20,21]. Correctly assessing genetic variation 
at MHC loci is likely to remain a key component of future 
research, both in basic sciences like immunology, ecology, 
evolution, and behavior, as well as in translational research such 
as finding the genetic basis of various immune disorders and 
wildlife diseases. 

Although high-throughput, locus specific methods for genotyp- 
ing of human MHC (HLA) loci have recendy been made available 
[22,23], accurately genotyping MHC loci in non-model organisms 
remains a complicated challenge [24]. The biggest barriers to 
genotyping MHC genes occur at the PCR stage, because the 
MHC genes that encode the antigen binding regions often exist in 
multiple paralogous copies within genomes [2,25-27], making 
traditional cloning followed by Sanger sequencing problematic. 
Ideally, a different pair of forward and reverse PCR primers would 
be used to individually amplify each MHC locus before 



July 2014 I Volunne 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

sequencing, allowing for the unambiguous characterization of 
indi\'iduals as hetero- or homozygous at each paralogous copy 
[28-30]. In practice, a locus by locus approach is usually not 
feasible, for several reasons. First, MHC loci often share allelic 
lineages (groups of similar alleles that are highly divergent from 
other such groups) which can persist even beyond speciation 
events [31-33]. Second, different MHC loci can exhibit substan- 
tial sequence similarity due to the high rates of inter-locus 
recombination and gene conversion [34—36]. Third, many MHC 
gene duplications are of recent origin, making it less likely that 
there ^\ill l)c fixed serjuence differences between paralogs where 
primers might be placed [25,32,37]. Combined, these factors can 
make it very difiicult to find unique primer pairs that amplify all 
alleles at a single MHC locus. In practice, the only option is to use 
primer pairs conserved across all the MHC loci of interest (i.e. 
class I or class II), meaning that all the MHC alleles present within 
a given individuals' genome are amplified simultaneously [24]. 
Further challenges to genotyping MHC in non-model organisms 
are presented by the possible presence of pseudo-genes, loci that 
amplify at lower efficiency, and variation in the number of 
paralogs between species, populations, and individuals [16,24,25]. 

Given these challenges, sequencing MHC loci in non-model 
organisms has, until recendy, been accomplished by 1) extensive 
bacterial cloning and direct sequencing [33] or 2) conformation 
based detection methods [24,38-41]. The latter approaches rely 
on running PCR-amplified sequences through a charged gel 
matrix or capillary sequencer to identify alleles based on 
differences in DNA strand mobility. The main advantage of 
conformation based approaches (once perfected) is that many 
(10 s-100 s) individuals can be genot)'ped more rjuickly and 
cheaply relative to cloning and sequencing. The disadvantages are 
that (1) these methods often take significant time and effort to 
begin work in new systems (2) co-amplifying alleles are not always 
distinguishable from one another, (3) amplification bias can cause 
alleles to be missed, and (4) nucleotide sequences are not obtained 
without further sequencing, adding significant time and expense to 
fuUy characterize nucleotide variation [24,42] . Cloning, while not 
being subject to the same disadvantages as conformation based 
approaches, can require a very large number of clones to be 
sequenced for each individual wh(;n allelic diversities are high, 
making it both cost and labor intensive whenever multiple 
individuals need to be genotyped. Cloning can also introduce 
additional sequence artifacts due to mismatch repair of heterodu- 
plex molecules in the cloning process [43] . 

Given these limitations, researchers have recently taken 
advantage of next generation sequencing (NGS) technologies to 
genotype MHC loci [44-53]. NGS technologies allow researchers 
to directiy sequence individual PCR amplicons, performing the 
equivalent of milUons of cloning and sequencing reactions in a 
single sequencing run. Coupled with multiplexing of many 
barcoded samples, next-generation sequencing can allow for the 
complete sequencing of hundreds of individuals simultaneously 
[45,54,55]. Additionally, recent studies have shown that both 
cloning and conformation based approaches can significantly 
underestimate MHC diversity when compared to NGS approach- 
es [49,53], suggesting that the increased read depth of NGS allows 
for the identification of rare or less-efficientiy amplified MHC 

Next-generation sequencing has some drawbacks, however. 
Most notable, error rates for NGS are higher than Sanger 
sequencing. For example, 454 sequencing is subject to extensive 
homopolymer over- and underscoring [56], whereas lUumina 
sequencing is prone to substitution errors, with different types of 
substitutions (i.e. A to C) being more common than others [57]. 

Additionally, all next-generation sequencing approaches are stiU 
subject to artifacts generated during PCR. Of those, PCR 
chimeras [42,58] are the most problematic for MHC studies 
because they can be indistinguishable from naturally occurring 
intra and inter-locus recombinants, though they will typically 
occur at much lower fix-quencies [49,53]. Like previous approach- 
es, using NGS technologies does not allow one to assign alleles to 
specific loci or to determine zygosity across loci. Cost is also a 
major issue (although prices for next generation sequencing 
technologies decrease every yc-ar) and fjrces researchers to 
carefully balance the desire to obtain genotypes for many 
individuals within a single sequencing run versus the desire to 
increase the read coverage per individual [45,46] and to run 
duplicates of every sample to confirm genotypes [53]. 

Recentiy developed NGS-based approaches for genotyping 
MHC in non-model organisms [44,45,49,53] have all adopted an 
approach carried over from cloning and sequencing. These 
approaches attempt to classify each unique sequence returned in 
an NGS run as either a "true" alleUc sequence or as an 
"artifactual" one (a sequence containing errors). A given 
individual's MHC genotype then consists of all the unique true 
allelic sequences among all the sequences obtained for that 
individual in a given sequencing run. Although these approaches 
differ in the exact details, they all rely on two key assumptions: that 
sequences corresponding to alleles will be overrepresented relative 
to sequences corresponding to artifacts and that artifactual 
sequences will tend to be more similar to true allele sequences 
than true allelic sequences will be with each other [53]. Given 
these assumptions, recently developed approaches proceed via 
some combination of 1) filtering out low quality or obvious 
artifacts (i.e. sequences that are too short or long) 2) applying 
threshold criteria to sequences based on their frequency of 
occurrence (read depth) within an entire library or within a single 
sample library 3) applying criteria based on sequence similarity to 
differentiate artifacts from alleles, and 4) validating allele 
assignments using duplicate PCRs of the same sample. 

Existing methods suffer from some inadequacies resulting from 
violations of the two assumptions listed above. The assumption 
that true allelic sequences will be more frequent than artifactual 
sequences will not always hold. Some true allelic sequences will be 
represented by relatively few reads in the sequencing run, either 
from stochastic sampling from the sample library (i.e from the 
larger pool of PCR ampficons subsequentiy selected for sequenc- 
ing) or because some alleles amplify at low-efficiency relative to 
other alleles [53]. Additionally, some artifacts can be represented 
by a relatively large number of reads for a number of reasons. The 
occurrence of errors at early stages of PCR (and propagated by 
subsequent PCR cycles), the tendency of particular platforms to 
produce errors at certain points along a sequence (i.e. over and 
under-calling of homopolymer sequences), and the stochastic over- 
sampling of amplicons containing errors before sequencing can all 
produce a relatively large number of reads representing a single 
artifactual sequence. Consequently, there is a gray zone of 
moderately common sequences including both low-frequency true 
alleles and high-frequency artifacts [46]. Within this gray zone, 
applying a conservative frequency cut-off will increase the 
occurrence false-negatives (alleles being classified as artefacts) 
while more lenient thresholds will increase the number of false 
positives (artefacts being classified as alleles). 

The second assumption - that artifacts will be more similar to 
alleles than alleles are to each other - will obviously be violated 
whenever two alleles are relatively similar (i.e. <2 base pairs 
dififerent). The first methods to apply next-generation sequencing 
to MHC genotyping either ignored sequence similarity altogether 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

[45], or assumed that less frequent sequences that were nearly 
identical (<3 bp difference) to more frequent sequences were 
artifacts [44]. These shortcuts will necessarily lead to some 
instances where true alleles are classified as artifacts merely 
because they are similar to other true alleles, resulting in an 
increase in false negatives. 

Recently, Sommer et al. [53] proposed to address both the gray 
zone problem and the similar sequence problem by requiring that 
every sample be run in duplicate (i.e. two separate PCRs). This 
strategy works because, while a given artifact may be relatively 
common (i.e. fall in the gray zone) in one duplicate, it would be 
unlikely to be common in both dupKcates. Similarly, a true allelic 
sequence that appears at relatively low frequency in one duphcate 
is unlikely to do so in another (unless the low frequency is due low 
amplification efficiency and not stochastic sampling of PGR 
amplicons). For much the same reason, two similar sequences are 
likely to appear at high frequencies in both duphcates if both are 
alleles, whereas if one is an artifact it should appear at much lower 
frequency in at least one if not both duphcates. Although effective, 
there is one main drawback to running every sample in duplicate. 
Sample duplication reduces by half the number of samples that 
can be genotyped in a given sequencing run or for a given amount 
of sequencing money. This cost-benefit trade off may not make 
fiscal sense for many researchers, for example those interested in 
characterizing MHC diversity across many populations or who 
wish to have increased statistical power (by genotyping more 
samples) to detect the fitness effects of individual alleles within 

To address the problems described above we present a new 
method for genotyping MHC using next-generation sequencing 
technologies that we call Stepwise Threshold Clustering (STC). 
STC takes a fundamentally different approach to genotyping 
MHC loci than have previously published methods [44,45,53]. 
Rather than applying frequency or similarity criteria to determine 
the aUehc classification (true or artifact) of individual sequences, 
STC uses a clustering algorithm to group sequences into clusters 
based on sequence similarity. Crucially, unlike previous approach- 
es, the identity of true alleles is determined on a cluster by cluster 
basis rather than on a sequence by sequence basis. STC is 
designed specifically for situations where multiple loci are co- 
amplified and where multiple copies of the same allelic sequence 
may be amplified in a single sample. STC also does not assume 
equal amplification efficiencies for all alleles. The method is 
designed to be applicable to sequence data from any NGS 
sequencing platform, and can be applied to any number of 
samples for which MHC data have been recorded. Importantiy, it 
does not rely on including duphcate PCRs for each sample, 
making it comparatively cost effective. 

In this paper, we first outline the STC method in detail, showing 
how it works by applying it to reads generated from a single 
sample. We then applied STC to genotype MHC class II|3 loci in 
295 stickleback fish {Gasterosteus aculealus) collected from two sets 
of paired population inhabiting different freshwater habitats (one 
lake and one stream population per pair). Using the sequencing 
results obtained by STC, we are able to show that neighboring 
lake and stream populations differ significantly in their multi-locus 
MHC genotypes, and, using the increased power afforded by 
sampling so many individuals, we show that many alleles are 
significanfly more frequent in one habitat versus another when 
comparing paired populations. We verily the STC method by 
including results from six duplicate samples and by cloning and 
sequencing a small subset of individual samples. Lastiy, we discuss 
the a number issues arising from the analysis including variation in 
amplification efficiency, minimal sample library sizes necessary for 

genotyping, and the apphcability to non-pyrosequencing plat- 

Overview of Stepwise ThresKiold Clustering (STC) 

Although STC relies on the same two assumptions about the 
frequency and similarity of allelic and artifactual sefjuenc:es that 
previous methods do, it does not attempt to ascertain allelic status 
on a sequence by sequence basis. Rather, STC is based on the 
proposition that, for a single individual with N alleles, the 
sequences generated for that individual can be grouped into 
approximately N clusters of similar sequence reads. This is 
because, with the c-xccption of PCR chim(-ras, artifactual 
sequences wiU tend to be minor deviations from the sequences 
of true alleles. The approach taken by STC is to identify those N 
clusters for each individual sample, and to determine the identities 
of the true alleles from those N clusters. 

At the heart of the STC method is an algorithm that processes 
reads from each sample through successive rounds of clustering 
using increasingly stringent levels of sequence similarity. After each 
round, the resulting clusters are tested against two criteria to 
determine whether they correspond to one, and only one, of the 
original N alleles for that sample. First, a cluster must must contain 
enough reads relative to the sample library size (i.e. be large 
enough), which ensures rare but highly divergent sequences (e.g. 
PCR chimeras) are not counted as true alleles. Second, because 
the majority of reads in a given run are expected to be error free 
(estimated at 82 "/o of total reads for 454 pyrosequencing; Huse et 
al. 2007), a cluster representing a single true allele should contain a 
single "dominant" allelic sequence consisting of the majority of 
reads in the cluster and a much smaller frequency of derived 
sequences that represent artifacts. A cluster containing more than 
one dominant sequence likely contains reads derived from more 
than one true allele, in which case the reads in that cluster are re- 
entered into the algorithm for further partitioning. Once the 
clustering rounds are complete, the final result of STC is a set of N 
clusters representing the N true allelic sequences for each sample. 

The clustering algorithm does two things that help to solve the 
gray-zone and similar allele problems. First, clustering similar 
sequences together means that the more frequent artifacts are 
clustered together with the actual alleles from which they are 
derived, and thus artifacts wiU not necessarily be mistaken for 
alleles just because they are relatively common. Similarly, less 
frequent allelic sequences will end up forming their own distinct 
clusters with related artifactual sequences, even if the sizes of those 
clusters are relatively small. Second, by applying criteria to 
establish whether there is more than one dominant allele in a given 
cluster, true alleles with similar sequences can be differentiated 
from one another by refining the clustering until two legitimate 
clusters are formed. Clustering also has one additional advantage 
in that clusters that do not meet initial size criteria can be kept for 
cross-checking with known true alleles after clustering is complete. 
Such "small" clusters might represent artifacts - e.g. chimeras or 
divergent sequencing artifacts that appear early enough during 
PCR to generate many reads — or true alleles whose amplification 
efficiency is much lower than other alleles present in the original 
sample. If a given small cluster from one sample appears as a large 
cluster in multiple other samples, it can be assumed that the small 
cluster represents a true allele whose small cluster size was likely 
due to stochastic sampling effects. In essence, a strict size criteria 
can be applied during clustering to reduce the accumulation of 
false positives (small clusters that do not represent alleles), while 
cross-checking the resulting small clusters against the all samples 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Teclnnology 

can substantially reduce the number of false-negatives (true alleles 
not recognized as such). 

Methods and Materials 

Ethics Statement 

This study was carried out in accordance with the protocol 
approved by the University of Texas Institutional Animal Care 
and Use Committee (permit # 07100201). Fish were collected 
using permit # SPR-0305-038 issued by the British Columbia 
Ministry of Environment. 

Data and Script Availability 

Raw and processed data files and all scripts necessary for 
running the STC algorithm have been made available at the 
Dryad Digital Repository ( 
4fn4g). Users can use the provided scripts and data to generate 
the genotyping results described below, as well as applying them to 
their own data. Instructions for using the scripts are provided in 
the README file included in the repository. 

Sample Collection 

We collected 364 threespine stickleback {Gaslerosteus aculeatus) 
from four different populations on northern Vancouver Island 
(Table 1). Upon capture all fish were euthanized with a lethal dose 
of MS-222. Caudal fin clips were taken from each fish and stored 
in 90% ethanol for later DNA extraction. DNA was extracted 
from whole fin clips using Wizard Genomic DNA Purification Kits 
(Promega ^A1120), following the protocols indicated by the 
manufacturer for animal tissue extraction. Initial DNA concen- 
trations were obtained using Quant-iT PicoGreen kits (Invitrogen 
PI 1496) following manufacturer protocol. 

PCR and Sequencing 

It has been previously estimated that stickleback could have as 
many as six [59] and as few as two to four [36,60] different MHC 
class up loci. The publicly available reference genome assembly 
for stickleback [61] contains 5 annotated and 1 unannotated 
MHC class 11(3 loci with associated ESTs [60]. The highly 
variable, polymorphic binding region in stickleback is located in 
exon 2 [59] . We designed forward and reverse primers that were 
conserved across these six MHC class IIf5 loci, meaning these loci 
should amplify simultaneously [42,62]. Our forward primer 
sequence (5'-TGTCTTTAACTCCACGGAGC-3') sits 32 base- 
pairs downstream from the start of exon 2, while our reverse 
primer sequence (5'-CTCTGACTCACCGGACTTAG-3') spans 
the boundary between exon 2 and intron 2. The amplicon 
generated by these primers is 213 base pairs long (253 base pairs 
including primers) and constitutes 70 (81 including primers) of the 
92 amino acids of exon 2. Note that these primer sequences are 
very similar to those developed independently by Lenz et al. [42] 

Table 1. Population sampled and numbers of samples collected. 

for amplifying stickleback MHC class Ilfi exon 2 sequences 
intended for reference strand-mediated conformation analyses 

Each forward and reverse primer also contained a 15 bp 
barcode at the 5' end (Table SI). Each sample in our 
pyrosequencing run was initially amplified using a unique 
combination of forward and reverse barcodes so that reads 
associated with individual samples could be identified after 
sequencing. By using 20 (or more) barcodes on the forward and 
on the reverse primers we are able to multiplex up to 400 (or more) 
uniquely barcoded individuals into a single sequencing library. 
Our barcodes consisted of 10 base pair MID tags supplied by 
Roche for amplicon pyrosequencing, with an additional 5 base 
pairs from the beginning of another MID tag added on to the 3' 

PCR reactions were performed in 50 ul total volume containing 
25 ng of extracted DNA, 10 uL of lOX (-MgC12) PCR buHer 
(Invitrogen), 300 |xmol of MgC12 10 |Xmol dNTPs, 20 ^mol each 
of forward and reverse primers, and 1 unit of Platinum Taq DNA 
Polymerase (Invitrogen). The PCR program used for all samples 
was: initiahze at 94°C for 120 seconds, 25 cycles of denature at 
94°C (30 seconds), anneal at 57°C (30 seconds), and extension at 
72°C (60 seconds), and a fmal elongation at 72°C for 240 seconds. 
Lenz and Becker [63] were able to substantially reduce the 
number of PCR chimeras when sequencing stickleback MHC class 
II using 25 PCR cycles and a 60 second extension, both of which 
we applied here. After PCR, samples were cleaned using 
Agencourt AMPure XP PCR purification (Beckman Coulter) 
according to the manufacturer's instructions, re-quantified using 
the Quant-iT PicoGreen kits used previously, and finally pooled 
into a single library at equimolar concentrations for sequencing. 
Samples were sequenced at the University of Texas Genome 
Sequencing and Analysis Facility on a Roche/ 454 FLX sequencer 
using titanium chemistry and standard amplicon pipeline proce- 
dures. The entire library was run on one-quarter of a picotiter 

Stepwise Threshold Clustering 

STC can be broken down into four phases: 1) sequence 
preparation, 2) sequence combination, 3) stepwise clustering, and 
4) post-clustering processing (Fig. 1). Phase 1 consists of filtering 
reads and partitioning sequences among samples based on barcode 
sequences and can be performed using custom scripts or publicly 
available software [47] . Note that two phases of STC (2 and 3) are 
applied in succession to all individual sample libraries before 
moving on to phase 4. Commonly used terms and their description 
are provided in Table 2. 

STC Phase 1: Sequence Preparation 

The STC process starts with the raw read data in the form of a 
multiFASTA (.fna) fde derived from the sff file generated by a 


UTM coordinates 

Samples amplified 

Samples genotyped 

Farewell Lake 

314926E, 5564416N 



Farewell Stream 

314004E, 556461 4N 



Roberts Lake 

318053E, 5566856N 



Roberts Stream 

316975E, 5567731 N 



UTM coordinates are zone lOU. 
doi:l 0.1 371/journal.pone.Cl 00587.1001 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

Phase 1 : Sequence Preparation 

parse pyrosequencing output into 
multifasta file containing all 

sequence reads 

filter reads by read length (> 90% of 
total), and sort by sample 

Phase 2: Sequence Combination 


align all sequences in sample library 

Phase 3: Stepwise Clustering 

create similarity matrix for all 
sequences in sample library 

choose starting similarity threshold 
(y) for clustering 


set size (6) and dominance (5) 

repeat at sucesslvely larger 


create list of all possible pairs of 
sequences and type each pair 


combine reads from pairs that meet 
combination criteria 

Phase 4: Processing 


cross-check small clusters against 
common true alleles 

cluster reads at given level of 

apply size and dominance criteria to 
resulting clusters 

remove good and small clusters 
from clustering algorithm 

divide remaining ambiguous 
clusters and apply size criterion to 

remove probable chimeras from true 

assign good clusters true allele 
status based on dominant sequence 

Figure 1. Outline of steps in STC genotyping. Programs and software used to implement each step are given in italics. The initial sff file was 
parsed using proprietary Roche software at the University of Texas Genome Sequencing and Analysis Facility. Filtering of reads and parsing samples 
by barcodes was accomplished using a custom perl script. Phases 2-4 were implemented in a custom R script. All scripts necessary for running STC 
have been uploaded to the Dryad Digital Repository (http;// 0.5061 /dryad.4fn4g) 
doi:1 0.1 371/journal.pone.01 00587.g001 

single 454 sequencing run. We used a custom Perl script to parse 
this file (available at the Data Dryad Repository: http://dx.doi. 
org/10.5061/dryad.4fii4g). Each read corresponds to the se- 
quence generated within a single well of pyrosequencing. This 
script identified the forward and reverse barcodes, and the 
amplicon sequence (hereafter, just sequence) for every individual 
read. Any read not containing a uniquely identifiable forward and 
reverse barcode was discarded. In addition, we apphed a minimal 
read length filter, keeping all reads with intra-primer sequence of 
at least 200 base pairs (~94% of the amplicon length). This was 
done to ensure that short reads derived from different sequences 
were not classified as the same sequence due to missing base pairs. 
Stricter criteria could be applied, such that only sequences within 
three base pairs of the expected amplicon length could be 

included. However, the efficiency of cluster classification is 
improved by including more reads. Once reads have been 
minimally filtered by length and organized by sample, and 
forward and reverse primer and barcode sequences removed, the 
reads are ready for phase 2. 

STC Phase 2: Sequence Combination 

Previous approaches to genotyping typically flag sequences 
containing indels as artifactual and remove them from the analysis 
[44,45,53]. In STC we take a difierent approach. In phase 2, pairs 
of sequences are "combined" when one member of the pair has 
the correct number of basepairs (i.e. no open reading frames or 
stop codons) while the other member has an incorrect length but is 
otherwise identical or nearly identical to the first member. By 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Teclnnology 

Table 2. Commonly used terms and definitions. 




one individual to be sequenced (human, fish, mouse, bird, etc.) 

sample library 

all reads produced in a given sequence run for a single sample 

run library 

all reads produced by a single sequencing run (includes all samples) 


individual, non-unique sequence produced during sequencing corresponding to the sequence of a single PCR amplicon 


unique read produced during a sequencing run (many reads can correspond to the same sequence), and indicated by an # (e.g. #130) 

true allele 

sequence inferred to match an allelic sequence in the genome of a given sample 


sequence inferred to not match an allelic sequence in the genome of a given sample 


group of similar reads (or a single read) produced during phase 3 of STC 

dominant sequence 

sequence with most reads in a given cluster 

subdominant sequence 

sequence with second most reads in a given cluster 

dropped allele 

an allele originating from a small cluster in phase 3 that was made a true allele during phase 4 

missing allele 

an allele present in the genome of the sample does not appear as a true allele after STC is complete 


combine, we mean that the reads associated with the second, 
artifactual member are converted to reads of the first, correct- 
length sequence. 

The rationale behind this phase is straightforward: artifactual 
sequences contain important information about which sequences 
represent true alleles. This is especially true for reads obtained 
using 454 pyrosequencing, as the most common sequence errors 
generated during pyrosequencing are homopolymer insertion/ 
deletion errors [56], which occur when the number of successive, 
identical base pairs in a sequence are over or under-called relative 
to the true sequence. By combining artifacts with very similar 
sequences from which they are clearly derived, we increase the 
weight of evidence that the more frequent sequence is a true allelic 
sequence. This tends to reduce ambiguity in the subsequent 
clustering phase, because true allelic sequences will be more 
dominant relative to other sequences in their cluster after being 
combined with clearly artifactual sequences. 

To combine sequences we first divide all possible pairs of 
sequences in a sample library into five pair types: (I) pairs that 
differ by only an indel (II) pairs that diflFer by one insertion and one 
deletion (III) pairs diflFering by only one indel and one substitution 
(IV) pairs that differ by one indel and two substitutions (V) all other 
pairs, which are not combined. In types I, III and IV, the fu-st 
member of the pair is always the sequence of the correct length. In 
type II, where the lengths are the same, the first member is always 
the more common sequence. We then apply three criteria to each 
pair to determine whether they should be combined. First, the first 
member of the pair must have the correct number of base pairs for 
the sequence of interest (e.g. 213 bp for our sequences). Second, 
the first member of the pair must be more common than the 
second member. Third, parrs can only be combined if the second, 
derived member is unique to that pair within that type. If, for 
example, one type III pairs contains sequences X and Z, and 
another type III pair contains Y and Z, then it is ambiguous 
whether Z is derived from Y or X and neither pair is combined. 
Finally, we note that, because every possible pair of sequences is 
evaluated before combining pairs, the length of the time for phase 
2 increases with the square of the number of unique sequences 
present in the sample library. 

STC Phase 3: Clustering 

The STC algorithm uses a variation on a formal Dirichlet 
process known colloquially as a Chinese restaurant table process. 
In the restaurant analogy, imagine 100 customers wish to enter a 
restaurant that can contain an infinite number of tables. The first 
customer enters the restaurant and sits at a table. The second 
customer enters and can choose to start a new table or to sit at the 
existing table. Every subsequent customer enters the restaurant 
and makes the same choice — sit at a new or existing table. 
Whether or not each new customer chooses to sit an existing table 
is directly proportional to how many customers are already at the 
table when the new customer enters. In a formal, discrete time 
restaurant table process objects (customers) start a new group 
(table) at some constant probability, or join an existing group 
(table) with a probability proportional to the size of each group (i.e. 
number of customers already seated at each table). The end result 
once all objects have been grouped is a set of groups that vary in 
the number of objects they contain. 

Our clustering process uses a similar mechanism to form clusters 
by sequentially taking each read (customer) in a sample library and 
either using it to start a new cluster (table) or allowing it to join an 
existing cluster (table). The process is quasi-Dirichlet because the 
reads (customers) are not clustered using probability rules based on 
cluster size. Rather, reads are added to clusters based on sequence 
similarity criteria. Specifically, at the point at which a given focal 
read is introduced, each existing cluster is assigned the sequence of 
the most frequent read contained within said cluster. For example, 
if cluster A contains two reads corresponding to sequence X and 
one corresponding to sequence Y, the cluster takes on the identity 
of sequence X (i.e. cluster A is assigned the sequence of X). The 
given focal read then either added to the most similar existing 
cluster, assuming the similarity between the focal read and the 
cluster is above predefined similarity threshold (y, see Table 3 for 
list of parameters set by the user) or it starts a new cluster. Note 
that once a read has been placed in a cluster, any reads sharing the 
same sequence wiU automatically be placed in that cluster when 
they are introduced into the process, meaning reads with the same 
sequence wiU never end up in different clusters. We note that this 
process is very similar to a process independently developed by 
Prosperi et al. [64] to delineate HIV and Hepatitis-C viral 
sequences into different subtypes. 



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A New Method for Genotyping MHC Using Next-Gen Sequencing Teclnnology 

Table 3. User-defined parameters for STC. 




Recommended starting value 

Value used 


similarity threshold 

minimum similarity required between focal 
read and cluster for read to join cluster, 
increases in successive clustering rounds 

start at minimum similarity between 
two reads in the given sample library 
(e.g. 60%) 

Varies between samples 


size threshold 

minimum ratio of reads in a cluster to reads 
in a sample library necessary for the cluster to 
be classified as "good" 

1 /maximum expected number of 




minimum ratio of dominant to subdominant reads 
necessary for the cluster to not be classified as 




common allele 

an allele must be present in at least this many 
samples to be considered common for the 
purposes of cross-checking in phase 4 



Recommended starting values are offered because the optimal values will vary both by data set and the degree to which the user wishes to balance false positives and 
false negatives. Values used for the data set presented here are given in "value used" column. 
doi:l 0.1 371 /journal.pone.Ol 00587.t003 

STC is designed to identify clusters representing alleles that are 
very dissimilar from other alleles before gradually breaking apart 
clusters representing very similar alleles. To accomplish this goal, 
the sequence similarity threshold (y) for joining existing clusters is 
gradually increased over successive rounds of clustering. At the 
beginning of phase 3, y takes the similarity between the most 
dissimilar reads for a given sample and increases by a set amount 
(e.g. 1 %) during each successive round of clustering. This means 
that each successive read is more likely to form a new cluster in 
later rounds than in earlier ones. Clustering can, in theory, 
continue to y = 100%. In practice it is much better to end slightly 
below this level (y = 97%) in order to avoid separating artifactual 
sequences with minor errors from the clusters to which they 

Note that whether a focal read joins an existing cluster or starts 
a new cluster is entirely deterministic and depends only on the 
predefined sequence similarity threshold (which differentiates STC 
from a formal Dirichlet processes). However, during each round, 
each focal read is chosen at random from the remaining pool of 
reads until all reads are clustered, introducing a small amount of 
stochasticity into the final cluster configuration (sometimes two or 
three different configurations can occur depending on the order of 
clustering and value of y). To account for variation introduced by 
random ordering of reads, we repeat the clustering 100 times 
during each round, starting with a randomly chosen sequence 
each time. The most common cluster assignment (the mode) for 
each read among the 100 rephcates is used to determine to which 
cluster each read is assigned in each round. 

At the end of each round, every cluster assumes the sequence 
identity of its most frequent read and is assigned to one of three 
categories - good, small, or ambiguous - based on the two criteria. 
The first, which we refer to as the size criterion, states that a cluster 
must contain a certain proportion, 9, of the sequence reads from 
the focal sample library. The second, the dominance criterion, 
states that the frequency of the most common (dominant) read in a 
cluster divided by the frequency of second most common read 
(subdominant) must be greater than the threshold ratio 5. Both 9 
and 5 are set before clustering begins and do not change between 
rounds of clustering. Clusters that meet both criteria after each 
round are classified as good clusters and all reads contained 
therein are exempt from further rounds of clustering. Clusters that 
meet the dominance criterion but not the size criteria are 
considered small clusters and are also considered exempt. Clusters 
that do not meet the dominance criterion (e.g., contain two or 

more abundant sequences), potentially contain more than one true 
allele. These clusters are classified as ambiguous and are retained 
for the next round of clustering at a more stringent threshold 
whether or not they meet the size criteria. 

Although the thresholds associated with the two criteria must be 
set heuristicaUy by the user, they can be adjusted to better trade-off 
false positives and false negatives. Setting 9 too low means some 
small clusters that actually derive from artifactual sequences may 
be categorized as good (an increase in false positives). Alterna- 
tively, setting 6 too high will result in an increase in false negatives, 
although this increase can be mostly be offset during phase 4, 
meaning its better to set 9 somewhat conservatively. For example, 
given no stochastic or amplification bias effects, the lowest 
proportional size of each cluster will simply be one divided by 
the maximum expected number of alleles (Nm^x, assuming no 
homozygotes). Because there will inevitably be some clusters 
smaller than that the 1/N„,ax ratio due to amplification bias or 
stochastic sampling of reads from the library, an appropriate 
starting value for 6 would be to divide l/N„-;i„ again by some 
constant C to reduce the minimum size a bit further. For example, 
with 6 different MHC loci, N^j^^^ 12. An appropriate starting 
value for 9 would be 1 divided by 12 divided by again 2 or 1/24. 
In this instance, the size criterion states that a cluster must contain 
at least 1/24 of the total reads for that sample. An appropriate 
starting point for 5 is to consider that experiments have shown that 
about 18% of reads in a given run represent artifacts [56]. This 
means that the ratio of the dominant sequence to the subdominant 
in a cluster will likely be on average no more than 0.82/ 
0.18 = 4.55, and usually much greater, so 5 = 4.55 makes a good 
starting value. 

Finally, after clustering is complete, it is possible that some 
clusters will remain classified as ambiguous. Either these clusters 
represent two very similar alleles, one allele with an additional 
frequent artefact, or zero true alleles. These clusters are dealt with 
as follows. Each ambiguous cluster is divided into two sub-clusters 
whose size (number of reads) is proportional to the relative 
frequencies of the two most frequent reads (dominant and 
subdominant) in the cluster. These two sub-clusters are then 
checked against the size criterion. If a sub-cluster passes the size 
criteria, then it is considered a good cluster. Otherwise the sub- 
cluster is classified as a small cluster. Additionally, it is sometimes 
the case that one of the top two sequences in a cluster will not be 
the correct length, in which case the third most frequent sequence 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

is treated as the subdominant sequence and the same rules are 

STC Step 4: Post-clustering processing 

Phase 4 consists of two stages: cross-checking all small clusters 
against good clusters and checking allele sequences for possible 
chimeras. As noted by Sommer et al. [53], some adleles may tend 
to not be genotyped due to low amplification efficiencies, or alleles 
may be missing from sample libraries simply due to chance (we 
refer to such cases as "missing" alleles). In other cases alleles 
present in the genome of the sample will be present in the sample 
library but won't produce enough reads to pass the size criterion 
(we refer to such cases as "dropped" alleles). To check for dropped 
alleles, small clusters are cross-checked against commonly occur- 
ring good clusters across all samples. Good clusters are classified as 
common if they occur in at least s samples after phase 3 (e.g. at 
least three other samples). If the identity of a given small cluster 
matches a common good cluster, then it is assumed that the cluster 
"dropped out" due to stochastic effects and can be included in the 
list of good clusters for that particular sample. 

Cross-checking small clusters in this way also has an added 
benefit, because the frequency at which a cluster drops out during 
phase 3 provides information as to whether the allele likely 
amplifies with low efficiency. Because such alleles will tend to drop 
out more frequendy than other alleles for a given value of 6, cross- 
checking clusters also allows the user flexibility in adjusting the 
value of 9 to control the rate of false positives and false negatives. 
Users can be relatively conservative with assigning the value of 9, 
knowing that dropped clusters (i.e. potential false-negatives) can be 
cross-checked and included in the final good cluster at the end. 
Once small clusters have been cross-checked against common 
good clusters, all good and dropped clusters for each sample are 
officially assigned true allele status, whereby the dominant 
sequence in each cluster is inferred to represent a true allelic 

Lastly, true alleles are checked to see if they are likely to be PGR 
chimeras. Chimeric sequences can occur during the initial PGR 
stage when incompletely extended primer sequences subsequently 
anneal to a different template in a later cycle, or by template 
switching during extension. In either case, the chimeric daughter 
strand will resemble one parent sequ(;ncc ()V(;r one portion of its 
length, and a different parent sequence over the other portion. If 
the number of PGR cycles has been kept to a minimum during 
read generation [63], chimeras are unlikely to be present in large 
numbers because heteroduplexes are more likely to form during 
the later stages of PGR [6.5] . Nonetheless, it is recommended that 
any alleles obtained for a given sample library be checked for 
possible chimeras. A number of common methods for detecting 
chimeric sequences have been published [66-68], although these 
were designed for large 16S rRNA data sets where the number of 
potential parent templates and sequences are large and unknown. 
In the context of MHC genotyping, alleles can be checked for 
chimeras using visual inspection of alignments [45], or with 
custom scripts that check alleles against all sequences present in a 
given sample library [53]. In STC, each true allele is scanned to 
see if it looks like a close recombinant (daughter sequence) of two 
other true alleles (parents) in the same sample. For each possible 
recombinant allele, all the samples containing that allele are 
scanned for the possible parent alleles. In cases where the 
recombinant alleles occurred with possible parent alleles in all 
cases, we classified the recombinant as a chimera and removed it 
from the final data set. Recombinant alleles that do not co-occur 
with possible parent sequences in all samples are assumed to have 

resulted from natural recombination/gene conversion events 
between loci and are not classified as chimeras. 

Amplification Efficiency 

To check whether variation in the rate at which alleles were 
dropped during phase 3 was potentially related to amplification 
efficiency, we estimated the correlation between average relative 
cluster size (proportion of sample library reads in a cluster) and the 
rate of dropping (fraction of total occurrences where an allck' was 
initially dropped in phase 3). For each allek", w(; (estimated the 
relative cluster size both overall and only in samples where an 
allele was not dropped. Alleles that are amplified at lower 
efficiencies should have both smaller relative cluster sizes within 
sample libraries (even in cases where those alleles are not dropped) 
and elevated probabilities of dropping compared to other alleles. 
We therefore expected a negative correlation between cluster size 
and rate of dropping if low amplification efficiency was causing 
some alleles to drop out more than would be expected by due to 
stochastic effects alone. 

Repeatability of STC genotyping 

Duplicate PGR reactions were run for 21 total samples. When 
both duplicates achieved the minimum sample library size, we 
compared the STC output for each duplicate to test the 
consistency of STC across different libraries for the same samples. 
We also cloned and sequenced four samples for verification of our 
genotyping method. We used the same PGR conditions used to 
amplify samples for pyrosequencing. The PGR products were 
purified using QIAquick PGR Purification Kits (Qiagen 28014) 
and cloned into a vectors using a pGR 2.1-TOPO TA kit j)r()vided 
by Life Technologies (K45()()()i). Aft("r o\'eriiight growtii. individ- 
ual clones were amplified using Ml 3 forward and reverse primers. 
Amplified clone sequences were purified using the same QIAquick 
kits and sequenced directiy on an Applied Biosystems AB 3730 
sequencer at the University of Texas IGMB DNA core facility. We 
originally targeted 100 clones per sample, but found that only 50— 
60 clones were needed to verify the most diverse sample. 

Relationship between read number and allele number 

Samples that yield fewer sequence reads than typical are prone 
to having alleles with zero corresponding sequence reads in their 
sample library (i.e. missing alleles) due to stochastic under- 
sampling of reads during sequencing. Moreover, the probability 
of such missing alleles wiU be magnified when those alleles also 
amplify will low efficiency. As a result, allelic diversities may be 
underestimated for some samples with small library sizes. To test 
for this possible bias, we estimated the correlation between library 
size (read number) and alkJe number within each of the four 
populations of stickleback. In any populations where the 
correlation was significant, we re-estimated the correlation using 
increasingly larger minimal samples sizes (up to 800 reads), to ask 
at what point the correlation was negligible or became statistically 
insignificant (keeping in mind that the power to detect a significant 
correlation wUl decrease as more samples are removed from the 
analysis due to an increased minimal sample size). 

Visualization of allele and cluster similarity 

In order to help visualize the clustering process for our 
individual example SEimple library, we created a two dimensional 
plot of all the sequences present in that sample library using 
multidimensional scaling (MDS). Briefly, MDS attempts to project 
the N-dimensional distances between objects (i.e. sequences) into a 
two dimensional space. Sequences placed closer together in the 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

space are more similar to each other than sequences placed further 
away. One advantage of using MDS to view sequence relation- 
ships is that it can be based on the same similarity matrix used by 
STC to cluster sequences, and thus provides a visual representa- 
tion (though not complete replication) of the STC process. We 
used the pcoa function in the R package ape [69] to generate 
principal coordinate axes of genetic similarity based on the 
percentage of shared base pairs. The first two MDS axes can be 
used to create a plot of genetic similarity. In addition to the MDS 
plot, we also created a neighbor-joining tree of the most common 
sequences across all samples to visual sequence similarit)' among 
alleles. We used the hionj function in the ape package, which 
implements the method of Gascuel [70] for producing neighbor 
joining trees, and the plot.phylo function for visualizing the tree. 
AH visualizations were done using the R statistical programming 
language, version 2.15.1 [71]. 

Statistical comparison between populations 

We used the results produced by the STC algorithm to make 
three different comparisons between our four populations. First, 
we used an ANOVA to determine whether our populations 
differed in the mean number of alleles per fish. Habitat (lake or 
stream), population pair (Roberts or Farewell), and their interac- 
tion were used as factors in the ANOVA. Second, it has been 
hypothesized that divergent selection among habitats (due to 
contrasting parasite communities) could lead to the divergence in 
MHC genotypes. We therefore tested whether our lake and stream 
populations differed significantly in overall MHC allele composi- 
tion using the GLM-based approach advocated by Warton et al. 
[72] and implemented in the R package mvabund [73]. This 
approach first uses separate GLMs to estimate the effects of 
predictor variables (i.e. habitat and pair) on the probability of 
having each MHC allele. Individual test statistics (e.g. likelihood 
ratios or wald statistics) from each GLM are then added together 
to create an overall test statistic for each predictor, and 
permutations of the original data are followed by recalculation 
of the overall test statistics to produce p-values. This approach has 
an additional advantage of controlling for correlations in the 
response variables among individuals, as might occur due to 
linkage disequilibrium among loci. Warton et al. [72] have shown 
their approach to be both more powerful statistically and much 
less prone to confounding dispersion and location effects than are 
similar approaches such as ANOSIM [74] or PERMANOVA 
[75]. Our p-values were calculated using 1000 permutations. 
Lastly, we used log-likelihood ratio tests (G-tests) to test whether 
the presence or absence of each allele was significantly associated 
with habitat within pairs. \\\' restricted this analyses to testing 
differences between lakes and streams within pairs. P-values were 
corrected for multiple comparisons by applying a false discovery 
rate of S'Ki [76]. All statistical analyses were implemented using the 
R statistical programming language, version 2.15.1 [71]. 

The most recently proposed method for using NGS to genotype 
MHC loci requires every sample to be run in duplicate [53], thus 
decreasing by half the number of samples that can be genotyped in 
a given run. We wished to determine the degree to which 
genotyping half as many samples per population would effect the 
probability of finding significant differences between habitats in 
individual allele frequencies using the analysis described abo\ e. 
We randomly sub-sampled half the samples from each population 
1000 times without replacement and recalculated p-values for each 
subsample. We then calculated the percentage of the 1000 
subsamples in which each allele was found to be statistically 
significantly different between habitats (after accounting for 
multiple comparisons within each subsample). We would expect 

that, with our increased sample size, we would be much more 
likely to identify alleles that differ significantly in frequency 
between habitats than if we had genotyped only half as many 


Illustration of STC using one sample 

Phase 1. A single stickleback sample (hereafter, sample X) 
from the Roberts Lake population (sample ID 490 in Table S2) 

was chosen to illustrate the STC process in detail. Sample X had, 
after initial quality and length filtering, 330 reads in its sample 
library. These reads corresponded to 101 unique sequences, 
including 72 sequences that only appear once in the sample library 
(Table S3 contains a summary of all 101 unique sequences 
associated with sample X). This sample was chosen partly because 
its 6 true alleles and 330 reads fell near the median of both 
distributions. More importantly, this sample illustrates clearly 
three of the processes unique to STC: 1) the gradual separation of 
clusters representing alleles as the similarity threshold y is 
increased, 2) the cross-checking of small clusters against common 
alleles to reduce false negatives, and 3) the delineation of two true 
alleles that differ by fewer than 3 base pairs. 

Phase 2. In this section, unique sequences wiU be referred to 
by the including a # at the beginning of the numerical sequence 
ID (i.e. #1234). Clusters of sequences (good, small, or ambiguous) 
are designated by an X at the beginning of a numerical sequence 
ID (i.e. X1234), where the ID refers to the dominant sequence in 
the cluster. 

Of the 101 unique sequences in the sample library, 59 were of 
the correct length (213 base pairs). Thirty-three sequences were off 
by a single base pair (212 or 214 base pairs), 7 by two base pairs, 
and 2 by three or more base pairs (Table S4). After aligning and 
checking all 3160 unique pairs of sequences for indel and 
substitution differences, we found 21 type I pairs (i.e. sequences 
differing by a single indel). One sequence (#26826) differed from 
two sequences by one indel and was combined with the more 
common of the two. Combining these remaining 20 type I pairs of 
sequences left 81 unique sequences in the sample library. We 
found 4 type II and 27 type III pairs which also met our criteria for 
combining pairs. This resulted in a further decrease to 67 unique 
sequences, while the total number of sequence reads remained at 
330. A summary of all pairs combined during phase 2 for sample 
X is contained in table S4. 

Phase 3. We set the size threshold for accepting clusters as 
good clusters at 6= 1/22 = 0.045 and the dominant to subdom- 
inant ratio threshold at 8 = 4/1. Note that these thresholds are 
only slightiy different than the baseline thresholds suggested in the 
methods and were determined heuristicaUy by re-running STC on 
a subset of samples to minimize false positives. The minimum 
sequence similarity among all pairs of sequences in the sample 
library was 60%, so we began clustc'ring at y = 60'M). Table 4 
contains a summary of clusters generated during each round of 
clustering for sample X (described below). Figure 2 visualizes the 
clusters in two-dimensional similarity space. 

As expected, clustering all 330 reads at y = 60% resulted in a 
single giant cluster. The dominant and subdominant sequences, 
#3111 and #103, were represented by 73 and 42 reads 
respectively. This single cluster clearly does not meet the 
dominance criterion (73/42= 1.74, which is smaller than 5 = 4). 
The same result was achieved when clustering their reads through 
y = 69%. At Y=70% sequence similarity, two clusters were 
formed. One cluster (dominant sequence #1238) consisted of a 
single sequence of 4 reads, which classified it as a small cluster (4/ 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 


/''""N #103 


' \ 
> * 1 

V /' 

^ #28260 ^ . / 

\ ' ^ ' / V 

\ ( • ; 



/ #195 


, Tr 1 1 O 

^ s 
/ s 

' A ^ 



' f } 

1 — 1 

UiUp|JcU allele 



'\ ' / 


during phase 2 


MDS Axis 1 

Figure 2. MDS plot of sequences in sample library X. Small black dots indicate the 101 unique sequences present in the sample library. 
Sequences have been plotted on the first two IVIDS axes generated using the same similarity matrix used during clustering, such that more similar 
sequences are closer together. The color of the larger circles indicates the final status of each sequence, whereas the size of each circle is proportional 
to the number of reads in the sample library that match that sequence (see Table S3 for list of sequences and their respective read numbers). 
Sequences indicated in red were combined with other more frequent sequences during phase 2. Sequences indicated in blue were deemed to be 
artifactual sequences. Allele #1238 (yellow) was not considered a good cluster in phase 3 (too small) but was considered a true allele after cross- 
checking in phase 4. The green circles indicate sequences corresponding to the other 6 true alleles. The dotted lines indicate the seven clusters (4 
good, 1 ambiguous, 2 small) after the 97% similarity threshold had been reached in phase 3. 
doi:1 0.1 371 /journal.pone.01 00587.g002 

330 = 0.012, which is less than 9 = 0.045). The other 326 reads 
were placed in a single large cluster whose dominant and 
subdominant sequences were again #311 and #103. This single 
ambiguous cluster was produced at similarity thresholds through 
y = 81%. 

At y = 82%, two clusters were produced. The #3111/#103 
cluster remained ambiguous as before. A second cluster with 
dominant and subdominant sequences #298 and #113 was also 
formed. However, this additional cluster did not meet the 
dominance threshold (30/27<4/l), and was also classified as 
ambiguous. At y = 85% the cluster dominated by #:31 1 1 split into 
two clusters, with dominant/subdominant sequences of #3111/ 
#5 and #103/#195 respectively. However, none of the three 
clusters passed the dominance criterion. At y = 88%, an additional 
cluster consisting of 3 reads of a single sequence (#28260) was 
formed and classified as a small cluster (3/330<0.045). 

At y = 90%, four total clusters were formed. The aforemen- 
tioned two clusters #3111/#5 and #103/#195 remained 
ambiguous. One of the other clusters, the cluster with the 
dominant sequence #298 met the size (41/330>0.045) and 
dominance (30/3>4) criteria and was classified as a good cluster. 
The cluster dominated by sequence #113 also met both criteria 
(31/330>0.045 and 27/l>4) and was also classified as a good 
cluster. At the final round of y = 97%, one of the previous 
ambiguous clusters (#103/#195) split into two distinct clusters. 

both of which passed the criteria and were considered good 
clusters. At this point there were 4 good clusters corresponding to 
sequences #298, #113, #103 and #195, two small clusters 
corresponding to sequences #1238 and #26820, and one 
remaining ambiguous cluster with dominant and subdominant 
sequences #3111 and #5 that differed by only two base pairs 
(Fig- 2). ^ 

To determine whether our remaining ambiguous cluster should 
be considered two separate clusters, we divided the total number 
of reads in the cluster (140) between the two dominant sequences 
in proportion to the number of reads for each sequence. The top 
two sequences accounted for 1 1 1 reads, of which sequence #311 1 
accounted for 73 reads (65.8%) and sequence #5 accounted for 38 
reads (34.2%). Thus, the two hypothetical sub-clusters were 
assigned 65.8% and 34.2% of the total cluster reads, giving them 
92 and 48 reads respectively. In this case, both sub-clusters had 
greater than 4.5% of the total reads in the sample library (335) and 
were classified as good clusters. Thus at the end of the phase 3 we 
were left with 6 good clusters representing sequences (X298, 
XI 13, X103, X195, X311, and X5), and two small clusters 
consisting of one rare sequence each (XI 238 and X28260). 

Phase 4. To cross-check our small clusters against common 
good clusters, we set 8=3, which means that a sequence would 
have to be the dominant sequence of a good cluster in at least 
three other samples to be considered common. Of the two small 



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A New Method for Genotyping MHC Using Next-Gen Sequencing Teclnnology 

clusters carried over from phase 3 in our example, good clusters 
containing #1238 were present in 3 other genotyped samples, 
whereas #28260 was not classified as a good cluster in any other 
sample. Therefore, we added the cluster representing sequence 
#1238 to the list of good clusters for sample X. The cluster 
containing #28260 was considered an artifact. At this point, we 
inferred that the dominant sequences in our seven good clusters 
represented true alleles originally present in sample X. None of the 
seven true alleles was classified as a chimera (table 5). 

Applying SIC to genotype stickleback from four 

We obtamed 206,453 sequence reads from one quarter of a 
complete pyrosequencing run. The raw sff file has been deposited 
at the NCBI sequence read archive (accession number 
SRRl 177032). After removing reads that had intra-primer 
sequences of less than 200 base parrs, we were left with 156,841 
reads (76% of total reads). Of those, 136,861 (66% of total reads) 
could be assigned to a specific sample based on barcode sequences. 
Of the initial 385 individual PGR reactions (364 samples plus 2 1 
duplicates), 359 had at least one read associated with them after 
initial filtering (Table S2). We set an initial cutoff of 80 reads per 
sample for subsequent genotyping using STC, leaving us with 301 
total samples (295 samples plus 6 duplicates) to be genotyped. The 
mean number of reads per genotyped sample was 442 (median: 
318, range: 81^687). Of the 301 samples to be genotyped, 20 had 
more than 1000 reads associated with them. We elected to 
randomly subsample without replacement 1 000 reads from each of 
these 20 sample libraries for genotyping. This substantially 
reduced the total run time of the STC algorithm (which scales 
exponentially rather than linearly with read number). After 
applying the minimum cutoff of 80 reads and sub-sampling to 
1000 reads, the mean number of reads per sample was 380. 

After STC was complete, we had identified 244 unique true 
alleles, of which 101 were present in only one sample ("single- 
tons". Table S5). We expected relatively few chimeras to be 
recognized as alleles because of the precautions taken during PCR 
and because chimeric sequences would tend to generate small 
clusters in samples where they occurred. We identified 1 8 potential 
chimeric (or naturally recombinant) alleles (Table 5). Seven of 
these alleles (six singletons) were present with potential parent 
sequences in 100% of the samples where they were identified as 
alleles. We removed these seven probable chimeric alleles from the 
final data set. Potential parent sequences were found in 50% or 
less of the samples in which the other 1 1 recombinant alleles were 
identified, suggesting these 1 1 alleles are likely naturally segregat- 
ing recombinants. Removing chimeric alleles left us with 237 true 
alleles overall, including 96 singletons. 

Of the remaining true alleles, 218 were the correct length of 213 
base pairs (Table S5). The remaining 19 true alleles were only 212 
basepairs long (15 singletons). Many of these 19 may be true false 
positives, while others could be naturally segregating variants, 
including one allele that appeared in eight different samples (allele 
#388, Table S5). Note that all of these 212 bp alleles had average 
cluster sizes greater than 0.065 - we used a cutoff of 0 = 0.045 - 
indicating that a more stringent size criterion for clusters would 
stiU include many of these true alleles with incorrect lengths. 
However, we conservatively removed all of these 1 9 true alleles 
from our final data set before performing our statistical analysis, 
leaving 218 true alleles including 81 singleton alleles. AH true allele 
sequences present in at least 5 samples and at least 2 1 3 base pairs 
long have been deposited in the NCBI GenBank (accession 
numbers KJ782461 - KJ782548). 


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A New Method for Genotyping MHC Using Next-Gen Sequencing Teclnnology 

Table 5. Alleles with recombinant sequences. 


total occurrences 
among all samples 

occurrences with possible psrent 

% of occurrences 
with parents 

Classified as 
















































































doi:1 0.1 371 /journal.pone.Ol 00587.t005 

increased sensitivity of NGS, which is more likely to detect alleles 
that amplify at lower efficiencies [53,63]. 

Dropped alleles and amplification efficiency 

After phase 3 we had identified 9561 total small clusters among 
all of our samples, most of which were inferred to be sequencing 
artifacts. However, of those 9561 small clusters, 229 were classified 
as true (i.e. dropped) alleles after cross-checking against common 
good clusters during phase 4. The average number of such 
dropped alleles was 0.76 per sample (range: 0-4). Of the 301 
genotyped samples, 164 (54%) had at least one dropped allele. Not 
all alleles were equally likely to be dropped. Of the 218 true alleles, 
1 70 were never dropped (Table S5). Of the remaining 48 alleles, 
the average percentage of samples where a given allele was 
dropped was 18%. The least frequently dropped allele was 
dropped in only 2.6% of the samples in which it occurred, whereas 
the most frequendy dropped allele (#670) was dropped in 73% of 
the samples where it occurred. A number of other alleles were also 
dropped at relatively high frequencies (>40%, Table S5), 
suggesting that they may be amplifying at relatively low efficiencies 
with our primer pair. None the less, given the diversity of MHC 
alleles overall, it is probably inevitable that some alleles wiU be 
amplified with relatively lower efficiency. More importantly, this 
result highlights the value of STC in diagnosing which alleles may 
be subject to amplification bias when genotyping MHC loci. 

Considering only the 48 alleles that were dropped at least once, 
we found a significant negative correlation between the percentage 
of samples in which the allele was dropped and the average cluster 
size for alleles (r=— 0.64, P= lxlO~ , Fig. 4). This correlation 
remained strong and significant if we calculated the average cluster 
size using only good clusters (r=— 0.44, P = 0.001, Fig. SI). 
However, the correlation seemed due in large part to a small 
group of samples that had both small cluster sizes and a greater 
propensity to be dropped than other samples (filled circles in 

The overall average number of alleles per sample was 6.62 
(mode: 6), although population averages ranged from 5.9 to 7.1 
(Fig. 3). This is about 1-2 more alleles on average than have been 
found in previous studies of stickleback MHC using conformation 
based methods [62,77,78]. However, significandy higher diversi- 
ties have previously been found using NGS compared to 
conformation based approaches in Scarlet Rosefinches [Carpoda- 
cus erythrinus) [49]. Our results, like theirs, are likely due to the 

Farewell Lake 

Roberts Lake 





2 4 6 8 1012 

number of true alleles 

1 — r 

2 4 6 8 1012 

Figure 3. Number of alleles genotyped per sample. Tfie red 
dasfied lines indicate tfie mean number of alleles per sample for eacfi 

doi:1 0.1 371/journal.pone.01 00587.g003 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Teclnnology 

Fig. 4). We were curious about whether such potential low- 
efficiency alleles shared any sequence similarity. Of the eight true 
alleles dropped in at least 40% of samples where they occurred, 
seven were placed together in a clade of sequences that were 
distinctly divergent from other alleles (Fig. S2). The average 
pairwise similarity between members of this group to all other 
alleles was 65%. By comparison, alleles within this divergent group 
shared on average 99% sequence identity (~2 basepairs differ- 
ence), whereas alleles not in the divergent group were on average 
87% similar. One possibility is that this group of alleles (which 
includes the above mentioned seven alleles plus two more) derive 
exclusively from a single MHC locus with slightly different primer 
sequences than the ones used here. Interestingly, 250 of 302 
genotyped samples had at least one of the nine divergent alleles, 
and no sample had more than two, lending some support to the 
hypothesis that the alleles originate from a single locus that 
amplifies with low PGR amplification efficiency. 


We sequenced bacterial clones from a total of four different 
samples (cloning results are summarized in Table 6). We initially 
targeted 100 clones per sample. In the case of two samples, we 
matched sequences to all alleles identified during STC after 5 and 
34 clones respectively, although we continued sequencing up to 53 
and 57 clones to catch any additional rare alleles potentially 
missed by STC. We found perfect congruence between the 
sequences identified by cloning and by STC in these two samples. 
We managed to isolate and sequence only 5 and 9 clones for the 
latter two samples due to bacterial contamination. For both of the 
partially cloned samples, aU the clones exactly matched alleles 
found by STC, except in one case where a clone was clearly a 
chimeric recombinant of two other sequences present in the 
sample. However, due to the limited number of clones sequenced, 
we were only able to match 4 of 6 and 5 of 1 1 alleles identified by 
STC in those two samples. Our cloning was able to identify a 


N ^ 

■« 03 

O 0 




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O Q 








percentage of samples 
where allele was dropped 

Figure 4. Negative correlation between average cluster size 
and frequency of dropping. Each point indicates a single allele. Only 
alleles that were dropped in at least one sample are plotted (n = 43). 
Cluster size is calculated as the average of the proportion of reads 
represented by the allele among all samples containing the allele. The 
solid line indicates the best-fit linear regression. The 95% confidence 
band for the regression is indicated in gray. Alleles associated with the 
"divergent" allele cluster (see Figure S2) are filled in black. 
doi:1 0.1 371/journal.pone.01 00587.g004 







^ 2 
c o 






July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

singleton allele present in only one sample in our run (allele ^1059 
in sample ID 1368), suggesting that, in principal, singleton alleles 
identified by STC are not necessarily artifacts of the sequencing 
process. Overall, none of the cloning results contradict any of the 
results from STC genotyping, albeit in a limited number of 

Duplicated Samples 

We originally amplified 21 different samples in different PGR 
reactions in order to compare the repeatability of the STC 
algorithm on the same samples (hereafter duplicates are referred to 
as "B" samples. Table S2). The results from our duplicated 
samples are summarized in table 7 . Of those 2 1 samples, only 6 
generated enough reads (>79) in both the A and B samples to be 
genotyped in duplicate. STC produced identical genotypes in the 
A and B samples in 2 of the 6 duplicate pairs. In 3 of the remaining 
4 pairs, the B sample was missing allele #83 or #655, two of the 
most frequently dropped (and divergent) alleles mentioned 
previously. In one case (sample ID 403), the B sample had one 
read representing the missing allele #83, but the cluster was not 
large enough to be good or to be added during phase 4 (i.e. at least 
3 reads in total). In the last duplicated sample (sample ID388), all 
three dropped alleles in the A sample (#83, #162, and #103) 
were not present in the B sample, suggesting that low efficiency 
accounts for their disappearance from the B sample as well. 
Overall, STC consistently identified the same true alleles in 
duplicate samples, with the exception of alleles that were identified 
(by STC) as alleles that tend to be dropped at high frequency. 
Additionally, it was not always the duplicate with more reads in 
which more alleles were identified, although alleles were generally 
not missing from duplicates that had at least 200 reads (Table 7). 
This implies (not surprisingly) that increasing the read number 
helps to reduce the likelihood of missing alleles that amplify at low 
efficiency. In fact, our duplicate results suggest that the frequentiy 
dropped alleles may, in fact, be missing (i.e. not be genotyped 
despite being present in the genome) at fairly high rates in samples 
with a small number of reads. 

Relationship between library size and allele number 

The correlation between sample library size (number of reads) 
and allele number ranged from r = 0.12 to r = 0.29 in the four 
populations (Fig. 5). The correlations were significandy different 
from zero in two of our four sampled populations, and marginally 
so in another (Fig. 5), indicating that the estimated number of true 
alleles generally increased with sample library size. Specifically, the 
expected number of true alleles at 80 reads and at 1000 reads 
increased by 0.8 alleles at the lowest (Farewell Stream) and by 2.1 
alleles at the highest (Roberts Stream). Recalculating the 
correlations after removing dropped alleles from the analysis 
resulted in non-significant correlations that are closer to zero for 
all four populations (Fig. S3). Overall, these results suggest there 
may have been some bias introduced by variation in read number 
with samples tending to miss alleles up to a certain minimum 
library size. 

To determine whether increasing the minimum sample library 
size could eliminate the correlations between library size and allele 
number we repeated the above correlation estimations for all four 
populations using a range of minimum samples sizes (80 to 600 
reads). In all populations, the correlation coefficient decreased as 
the minimum sample size increased, although the extent and rate 
of decrease varied among populations (Fig. 6). For both Roberts 
Stream and Farewell Lake, the correlations remained above 
r = 0.25 and were significant up to ~200 read minimum, at which 
point the correlations began to drop and become statistically 







m LT) m ro 
00 vo 00 CO 

^ ^ ^ 

fN ^ <— 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

Farewell Lake 

Roberts Lake 

co 8 ■ 

CD 2 ■ 


-Q 10- 
I 8' 
^ 6- 

o o 


o o ® 

OGffiD O 




cnxDO o 

ymo^o o o 

3C3D O 

r = 0.29 

C5D 00 
O as (3D 


(QpomociaciaO^ > e T 
AiAkdoo oo c 

3dooc!d qd o o oc 


r = 0.17 

Farewell Stream Roberts Stream 



o o 

o o o o o c 

o o oo o 

s>_(j|)^. £)a - - " 


DO c 

ooco o 

° r = 0.12 

o ° r = 0.28 

250 500 750 1000 250 500 750 1000 
number of reads 

Figure 5. Correlations between allele number and library size. 

Each point indicates a genotyped sample. Samples with more than 
1000 reads were sub-sampled to 1000 reads. No B (duplicate) samples 
were included to avoid pseudo-replication. The lines indicate the best- 
fit linear regressions for each population. Solid lines indicate 
correlations that are statistically significant at a = 0.05. Dashed lines 
indicate correlations that are not statistically significant. The 95% 
confidence bands for each regression are indicated in gray. 
doi:1 0.1 371 /journal.pone.01 00587.g005 

Farewell Lake Farewell Stream 




0.75 -r 
0.25- ^^^Jgr^ 

•i 0.75 

0 0.50-1 

1 0.: 


Roberts Lake 

Roberts Stream 


9^^Qq ooo 0 

CO 00. 



200 400 6000 200 400 
minimum sample library size 


Figure 6. Changes in the correlation between library size and 
allele number with different minimum sample library sizes. The 

correlation coefficients shown in figure 5 were recalculated at 
increasing minimum sample library sizes. Black circles denote statisti- 
cally significant (a = 0.05) correlations, whereas gray circles denote 
statistically insignificant correlations. The dotted line indicates a 
correlation of zero. The size of the circles is proportional to the sample 
size for each correlation. 
doi:1 0.1 371/journal.pone.01 00587.g006 

indistinguishable from zero. In the otlier two populations 
correlations were lower than 0.25 and remained statistically 
insignificant no matter the minimum library size used. Overall, 
these results suggest that, at less than 200 reads, some samples may 
be missing alleles, but that above 200 reads the bias introduced by 
sample library size was likely reduced. 

Statistical comparisons between populations 

Both population pair (Roberts vs. Farewell, F = 6.99, df=l, 
P = 0.008) and habitats (lake vs. stream, F = 9.53, df = 1, P = 0.002) 
differed from each other in the mean number of alleles per 
individual fish (Fig. 3). In particular, the Roberts fish had, on 
average, 0.53 fewer alleles than Farewell fish (6.93 vs. 6.39), while 
stream fish had, on average, 0.67 fewer alleles than lake fish. The 
interaction between pair and habitat was not significant (F = 0.22, 
df= 1, P = 0.64), indicating that magnitude of the difference 
between lakes and streams did not depend on the pair. 

Our populations also differed from each other in their overall 
MHC allele compositions (Fig. 7). Our two pairs were marginally 
significandy different from each other (wald = 9.73, P = 0.08) while 
lakes and streams were clearly significandy different in MHC allele 
composition (wald= 15.5, P<0.001). There was also a significant 
interaction between pair and habitat (wald = 6.51, P<0.001), 
which shows that the alleles were not diverging in frequency 
between habitats in a parallel fashion among our two parrs. 

In addition to the overall significant differences, we also found a 
number of alleles within each pair that differed significantly in 
frequency (percentage of individuals where the allele was found) 
between habitats (Fig. 7, Table S6, S7). In Roberts we found 28 (of 
1 34) alleles that differed significantly in frequency at 0( = 0.05, of 
which ten remained significant after controlling for multiple 
comparisons. Six of these (#40, #265, #347, #336, #103 and 
#132) were significantly more common in Roberts Lake whereas 
the other four (#120, #298, #670 and #707) were significandy 
more common in Roberts Stream. In Farewell we identified 2 1 (of 
1 28) alleles that were significantly different in frequency between 
the two habitats, of which seven remain significant after 
controlling for multiple comparisons. Four were more frequent 




CO 0.5' 








o o o"o " 


O O ' 



8 88" o° 

o o o o o 

_ 0 0.1 0.5 0.9 0 0.1 0.5 0.9 
proportion lake samples 

Figure 7. Differences in allele frequencies between lakes and 
streams. Allele frequency is calculated as the proportion of individuals 
carrying each allele in each population. Points closer to the dotted line 
indicated alleles with frequencies similar in the lake and stream 
populations. Red circles indicate alleles that were significantly different 
in frequency between lake and stream habitats (within pairs). Blue 
circles indicate insignificant differences in frequency. Filled black circles 
indicate that the allele was significantly different after controlling for 
multiple comparisons (false discovery rate = 5%). Note that frequencies 
are plotted on a logit (non-linear) scale. 
doi:1 0.1 371/journal.pone.01 00587.g007 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

in the stream (#175, #182, #655 and #659), while the other two 
(#1850 and #269) were more frequent in the lake. Taken 
together, these results suggest lake and stream populations have 
diverged in the relative frequency of certain MHC alleles 
(sometimes quite substantially) despite a lack of physical barrier 
to movement between habitats and previous evidence from neutral 
markers for gene flow between similar paired lake and stream 
populations [79]. 

Of the alleles that were significantly different in each pair after 
controlling for multiple comparisons, our sub-sampling analysis 
indicated that genotyping only half as many samples would allow 
us to find the same significant differences between 0 and 91.8% of 
the time for individual alleles in Roberts (Table S6) and between 0 
and 77.1 % of the time for individual alleles in Farewell (Table S7). 
Extending the analysis to alleles that were significandy different 
before adjusting p-values for multiple comparisons, we found 
significant differences in as few 0.2% of subsamples in Roberts and 
0% in Farewell. Using STC thus appears to provide substantially 
more power for testing hypotheses about MHC allele frequency 
difierences (and presumably other tests where power can be 
increased by increased sample size) when compared to methods 
where only half as many samples can be genotyped, provided the 
accuracy of the genotyping was roughly equal between the two 


Next generation technologies are quickly replacing traditional 
sequencing and conformation based approaches as the methods 
most suitable for sequencing multi-locus genes like those of the 
MHC [51,52,80]. In this paper we have proposed a new 
bioinformatic method for genotyping MHC genes using NGS 
technology and applied it successfully to a large sample of 295 
threespine stickleback. A handful of methods for correctiy 
genotyping MHC using NGS have been proposed in the last 
few years [44,45,53,80]. These methods have taken standard 
quality control approaches typically applied to cloning and 
sequencing and applied them to the output of next generation 
sequencers. The stated goal of these approaches was to use 
frequency and similarity criteria to correctly classify sequences as 
either artifacts or alleles, much as one would with sequences 
derived from individual clones. STC represents not just a variation 
or improvement on these methods, but offers a qualitatively 
different approach to genotyping. STC takes full advantage of the 
increase in sequence data acquired during next-generation 
sequencing by using a clustering algorithm to group together 
similar sequence variants. Because all sequences, artifactual or not, 
are derived from aUehc sequences originally present in the sample, 
there is information in the artifactuad sequences as well that can be 
used in genotyping alleles. 

Previous approaches to genotyping MHC genes using next- 
generation serjuencing technologies have typically struggled with 
two problems. First, there is a range of read frequencies within 
which there will be a substantial number of both true aUehc 
sequences and artifactual sequences, making it difficult to 
adequately balance the rate of false positives and false negatives 
during genotyping. Second, it can be difficult to distinguish 
whether two similar, but relatively frequent, sequences represent 
two alleles or one allele and one artifact derived from that allele. 
One recent previous approach [53] has proposed solving these 
problems by running duplicates of every sample, which, while 
effective, substantially reduces the number of samples that can be 
successfully genotyped in a given sequencing run. STC is able to 
overcome these problems in a number of different ways. First, true 

alleles wiU usually generate distinct clusters no matter what their 
frequency (unless they are just a few SNPs away from another true 
allele), whereas artifacts wiU generally cluster with the alleles from 
which they are derived unless they are chimeric or otherwise 
extremely error ridden. Second, read frequency criteria can be 
applied within clusters to reliably distinguish clusters that represent 
two aUeles versus one. Note that this discrimination occurs not just 
during the clustering in phase 3, but is dealt with impUcitiy in 
phase 2 during sequence combination, where frequent, but 
obvious, artifacts are combined with potential aUeles based on 
the error profiles of the given NGS technolog)'. Finally, STC 
aUows the user to be relatively conservative in applying aUelic 
status during phase 3, thus reducing false positives, and then aUows 
the user to recover many of the false negatives that may have 
dropped out in the subsequent phase 4, if those false negatives are 
found in other individuals from a population. 

Sample Library Size 

One concern of previous authors has been to ensure that 
enough reads are present in a given sample library to ensure 
accurate genotyping. Previous methods have taken a probabilistic 
approach based on multinomial distributions to determine the 
minimum number of reads required to estimate a genotype at 
some level of confidence [45]. Sommer et al. [53] take this 
approach a step further by incorporating estimates of relative 
amplification efficiency into calculating minimum library sizes 
necessary' to ensure, for example, 99% probability of not missing 
any alleles. While these recommendations are useful, especially 
when sequencing an organism for the first time, we beUeve they 
are too conservative for at least two reasons. First, Sommer et al. 
[53] base their recommendations on the maximum expected aUele 
number (in our case 12) and the minimum relative amplification 
efficiency of any allele to be included in an analysis (for which they 
provide ways of estimation after genotyping). This effectively 
targets the worst case scenario and likely overshoots the number of 
reads necessary to genotype most of the samples in the library, 
reducing the number of samples that can be genotyped in a given 
sequencing run. Moreover, the minimum number of reads 
required is highly dependent of the relative amplification 
efficiencies, which cannot always be estimated a priori, and for 
which there is going to be substantial variation between samples. 

Second, minimum size recommendations assume that identify- 
ing all aUeles for aU samples is necessary for accurately estimating 
all the parameters or testing aU the hypotheses one might be 
interested in. This is not necessarily true. If the goal is to provide 
estimates of population level diversity (i.e. how many alleles are 
present in the population) having some samples with incomplete 
genotypes due to having fewer reads is unlikely to change the 
population estimate. In fact, in such situations it would be better to 
aim for genotyping more samples than for increasing the number 
of reads for each sample, which increases the chances of finding 
rare aUeles. Alternatively, if the finad goal is to compare individual 
level diversity with some other measured phenotype (i.e. parasite 
burden), missing an allele in a few samples may alter the effect size 
estimate only slightly. In those cases, one possibility would be to 
use read number as a covariate in downstream analyses, or weight 
individual allelic diversities by the number of reads. If the final 
goal is to use the presence or absence of a given allele in 
association tests with other phenotypes (i.e. parasite infection), a 
smaU number of false negatives due to incomplete genotyping wUl, 
at worst, reduce statistical power but is unlikely to bias associations 
one way or the other. Our overall results suggest that ha\ ing at 
least 200 reads was sufficient for reducing the bias introduced by 
smaU library sizes, although even this number may be too large if 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

we remove the highly divergent, very low efficiency alleles from 
the analysis. Ideally, we would recommend targeting at least 50 
reads per allele for a sample with mean allelic diversity (i.e. 
~target 350 reads per sample if the expected a\'erage allele 
number of 7), or more as funds allow, because there will always be 
variability in the number of reads per sample in any given run. 

Amplification Efficiency 

Even if library sizes are adequate for genotyping, it is possible 
that some alleles may amplify with relatively lower efficiency, 
either because of differences in the primer or amplicon sequence, 
or because of difiFerences in PGR conditions between reactions and 
runs. STC allows for the identification of such alleles in two ways. 
First, alleles that amplify with low efficiency wUl have, on average, 
lower relative cluster sizes than other alleles. Second, alleles that 
tend to amplify with low efficiency will be more likely to be 
dropped during phase 3 before being added back to genotypes 
during phase 4. We showed that cluster size and rate of dropping 
in phase 3 were moderately negatively correlated in our data set, 
and plotting the relationship suggested that a group of highly 
divergent alleles were especially likely to be dropped (Fig. 4, Fig. 5). 
As suggested by our results from duplicated samples, it is possible 
that, in many cases, these alleles were not only dropped during 
phase 3, but were completely absent from some sample libraries. If 
the alleles in this group originated from a single divergent locus, it 
would mean that in 52 of 302 samples we did not identify any 
alleles from this locus. 

One of our divergent alleles (^^83) has been previously reported 
by Lenz et al. [42], who found this sequence in every one of 23 
cloned and sequenced three-spine stickleback samples (sequence 
was previously uploaded to GenBank: AF395709). Similarly, the 
same sequence was found in a highly conserved cluster of similar 
sequences in a sample of 30 threespine stickleback and in a sample 
of 30 nine-spine stickleback (Pungilius pungilius) [33]. These 
authors speculated that these sequences, because of their lack of 
diversity among individuals, could potentially originate from an 
invariant MHG class-II like locus involved in antigen processing 
[81]. Blasting these sequences against the published stickleback 
genome [61] reveals that they all clearly map to a single MHG 
class II locus (located on linkage group VII) with associated 
expressed sequence tags (ESTs). Using a comparative approach 
among teleosts, Dijkstra et al [60] argue that this locus is, in fact, a 
classical MHG class II locus (DB type) and not an MHG class II- 
Uke locus (DM type) that are thought to be involved in antigen 
processing. In our data set, this cluster of divergent alleles share, 
on average, only 65% sequence similarity with other class II 
alleles, contain no frame shifts (except in one case) or premature 
stop codons, and retain enough of the forward and reverse primer 
sequences to be amplified in most cases. However, without further 
information it may be difficult to determine whether these alleles 
are traditional class II or class Il-like alleles, but no evidence 
presented here would suggest they do not originate from a normal 
MHC class II locus. One possible explanation for their lack of 
genetic diversity is that their isolated genomic location relative to 
other MHG class II loci [60] may not predispose them to gene 
conversion or recombination events, which is thought to be one of 
the primary generators of MHG allelic diversity [35,82,83]. 

Single Read Alleles 

Aside from stochastic or amplification bias, cases where alleles 
are represented by only one or two reads could also result from 
sequencing errors in the barcodes such that reads are assigned to 
the incorrect sample (fairly unlikely given the redundancy of our 
barcodes). Alternatively, a very small amount of contamination 

during PGR setup could introduce alleles from other samples into 
the PG template. Previous approaches to genotyping have 
required at least three reads of a given sequence in at least two 

samples to consider it as a possible true allele [45,46]. In contrast, 
STC includes no such a priori filter. Although clusters must pass a 
size threshold before being considered good clusters (and thus true 
alleles), STC includes a cross-checking step that allows users to 
include small clusters as true alleles if they appear in larger clusters 
in other samples. In many cases this will result in a decrease in 
false negatives, because alleles that amplify at with low efficiency 
will often be represented by only a few reads, especially in sample 
fibraries of small to moderate size. In theory, small clusters 
represented by only a single read could be included as true alleles. 
We leave it to the user to decide how best to deal with such cases. 
We have taken the conservative approach of only adding small 
clusters as dropped true alleles if the total number of reads in the 
cluster is at least three. This is superficially similar to previous 
approaches, but note that only the cluster must contain at least 
three reads but that the reads do not have to represent the same 
sequence. Note also that, in STC, a sequence could be represented 
by two reads and be combined with a number of other sequences 
during phase 2 to increase its total read count well above three. An 
alternative approach would be to omit single read and double read 
clusters only when they do not represent low-efficiency alleles (as 
defined by the user based on average cluster sizes or frequency of 
dropping out during phase 3). A more probabilistic approach 
could, after phase 4, use multinomial probability distributions to 
omit clusters that would not occur in 95% of sequencing runs, 
assuming the same library size and number of alleles. 

Alternative Sequencing Technologies 

An important feature of STC is that it can be appUed, in theory, 
to any data set of MHG allele sequences (or other multi-allelic 
amplicon from a gene family) generated by NGS technology as 
long as 1) many reads can be generated for individual samples and 
2) individual sample libraries can be subset from the entire library 
using barcoding or other techniques. As other sequencing 
technologies increase their average read lengths, researchers will 
likely begin to shift away from pyrosequencing to alternative 
technologies (i.e. lUumina). There are a number of issues to 
consider when applying STC to data sets generated using such 
alternatives. First, lUumina sequencing typically produces many 
more reads per sequencing run than does pyrosequencing. 
However, sequencing facUities can often target a very specific 
number of sequences with barcoding, and thus users should be 
able to specify a smaller target number of reads based on the 
number of samples and desired coverage. Second, Illumina 
sequencing is more prone to substitution errors than to indel 
errors. In phase 2 of STG, sequences are combined when they are 
very similar but of different lengths, meaning with Illumina data 
few sequences may be combined. Future implementations of STC 
for Illumina data could skip phase 2 entirely, or phase 2 could be 
modified to combine sequences where a single substitution results 
in the creation of an open reading frame (i.e. a stop codon). Third, 
error rates tend to be an order of magnitude lower (0.3^% versus 
12%) than with pyrosequencing. This would mean that the 
proportion threshold (y) will likely need to increased to something 
around 9:1 rather than 4:1 as currently implemented for 
pyrosequencing. One of the overall advantages of STC is its 
flexibility, giving the user the ability to adjust the STG parameters 
based on the number of samples, the number of amplified loci, and 
expected error rates. Although we have not yet implemented STG 
for non-pyrosequenced data, we see no reason why STG could not 



July 2014 I Volume 9 | Issue 7 | e100587 

A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 

be applied directly to lUumina generated sequence data with 
minimal modiGcations to the STC protocol. 

Future Directions 

One of the advantages of STC is that all the reads prcsi^nt in the 
samj)lc library contribute to genotyping. This could provide a 
number of potential advantages beyond simply genotyping. First, 
by grouping reads into clusters that derive from true alleles, a 
better estimate of the relative amplification efficiencies could be 
estimated than if relative efficiencies are estimated only from reads 
that match alleles direcdy [45,53]. Second, current methods do 
not allow for estimation of allele and locus copy number within 
samples. Alleles may be present as homo or heterozygotes, or may 
be present at more than one locus due to gene duplication 
[25,32,37] . The number of loci may also vary between individuals, 
even within a single populations [16]. Currently, only the presence 
or absence of alleles can be inferred when loci are amplified 
simultaneously, and only a maximum MHC locus number can be 
inferred from the most diverse genotyped samples. It is possible 
that the data provided by grouping reads into clusters could be 
used in a full probabilistic model to simultaneously infer 
amplification efficiencies, allele copy, and locus copy number 
across all samples simultaneously. Such a model is beyond the 
scope of this paper, but we note that Dirichlet processes have been 
used as priors in Bayesian models infer HIV haplotype number 
from DNA isolated from infected patients [84,85] . We believe the 
success of these models suggest that STC, or similar, recentiy 
introduced methods [86], may, in the future, provide researchers 
not only with high-throughput genotyping of MHC in non-model 
organisms, but provide a fuUer picture of multi-locus MHC 
genotypes as well. For the present we offer STC as an efiicient, 
accurate, and extremely flexible method for genotyping MHC 
(and other multi-locus templates) using NGS which produces data 
that can be appUed to a variety of downstream parameter 
estimation and hypothesis testing applications. 

Supporting Information 

Figure SI Negative correlation between average good 
cluster size and frequency of dropping. Each point 
indicates a single allele. Calculation of cluster size averages do 
not include clusters originally dropped in phase 3. Each point 
indicates a genotyped sample. Samples with more than 1000 reads 
were sub-sampled to 1000 reads. No B (duphcate) samples were 
included to avoid pseudo-replication, he solid line indicates the 
best-fit linear regression. The 95% confidence band for the 
regression is indicated in gray. Alleles associated with the 
"divergent" allele cluster (see Figure S2) are filled in black. 

Figure S2 Unrooted, neighbor-joining tree of all alleles 
appearing in at least 10 samples. Dotted rectangle highlights 
group of highly divergent sequences (see also Table S5), all of 
which appear to amplify with lower efficiency than most other 
alleles. Three other alleles (two singletons) also belong in also 
group with these alleles, but were present in fewer than 10 
samples. Scale bar indicates 1% sequence similarity (~2 bp). 

Figure S3 Correlations between good cluster number 
and minimum library size. Plots are identical to figure 5, 

except that the y-axis shows only alleles identified through phase 3 
(i.e. good, but not dropped, clusters). Samples with more than 
1000 reads were sub-sampled to 1000 reads. Points represent 
individual genotyped samples. No B (duplicate) samples were 

included to avoid pseudo-replication. The lines indicate the best-fit 
hnear regressions for each population. The confidence bands for 
each regression are indicated in gray. 

Table SI List of barcodes used for 454 sequencing. 


Table S2 Genotyping results for individual samples. 


Table S3 Clustering results for individual sequences 
from sample X. 


Table S4 Combined pairs of sequences from sample X. 


Table S5 Table of alleles identified across all samples. 


Table S6 Allele frequencies, G-tests, and sub-sampling 
analysis results (Roberts). G-statistics are from tests of 

whether the lake and stream differ significantiy in allele frequency 
for each given allele. The proportion of sub-samples where sample 
size was divided in half (total = 10000) where the allele was 
statistically significant between habitats (a — 0.05) is given. Both p- 
values and proportion of significant sub-samples are shown when 
p-values are unadjusted and adjusted using a false discovery rate of 

Table S7 Allele frequencies, G-tests, and sub-sampling 
analysis results (Farewell). G-statistics are from tests of 
whether the lake and stream differ significantly in allele frequency 
for each given allele. The proportion of sub-samples where sample 
size was divided in half (total = 10000) where the allele was 
statistically significant between habitats (a = 0.05) is given. Both p- 
values and proportion of significant sub-samples are shown when 
p-values are unadjusted and adjusted using a false discovery rate of 

File SI R script (runSTC.R) for running phases 2—4 of 
STC. This script (along with the accompanying functions, will 
allow users to run phases 2-A of STC. A more thorough version of 
this file is available at the Dryad Digital Repository (http:/ / dx.doi. 
org/ 1 0.506 l/dryad.4fn4g), along witii a README.txt file tiiat 
contains more information on using the scripts and example data 


File S2 R functions to accompany the runSTC.R script 
(File SI). 



We would like to thank Hans Hoiinann lor crucial discussion during the 
early stages of STC developmenl and Scott Huencke-Smith for his 
expertise with NGS technologies. We would also like to thank Emily 
Parham, (jraham Segal, and Sam Thompson for performing the vast 
majority of the cloning. 

Author Contributions 

Conceived and designed the experiments: WES DIB. Performed the 
experiments: WES. Analyzed the data: WES. Wrote the paper: WES. 
Wrote scripts: WES. 



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A New Method for Genotyping MHC Using Next-Gen Sequencing Technology 


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